一种基于增强型最大二阶循环平稳盲解卷积的齿轮箱复合故障诊断

A Gearbox Composite Fault Diagnosis Based on Enhanced CYCBD

针对齿轮箱复合故障振动信号易受到背景噪声干扰,使得传统方法对复合故障冲击特征难以准确分离的问题,提出一种自适应最大二阶循环平稳盲解卷积(ACYCBD)与1.5维导数增强谱相结合的复合故障诊断方法。首先,利用循环谱分析检测复合故障振动信号中与故障特征相关的循环频率成分,构建不同目标类型的循环频率集;之后,根据不同类型的循环频率集,提出一种以三阶累积量稀疏度(TCS)为指标,自适应地选取最大二阶循环平稳盲解卷积(CYCBD)的最优滤波器长度的改进算法,从而更好地获得包含不同故障冲击成分的CYCBD最优滤波信号;最后,提出一种新的1.5维导数谱进行特征增强,提高信噪比,并分析谱图中突出的故障特征频率进而判别故障类型。通过仿真信号与故障实验平台数据对算法进行验证,结果表明该方法能够实现齿轮箱复合故障的准确分离与诊断。

Aiming at the problems that the gearbox composite fault vibration signals were susceptible to severe interference from background noise, which made it difficult to accurately separate the impact characteristics of composite faults with traditional methods, an adaptive CYCBD and 1.5-dimensional derivative spectrum were proposed for composite fault diagnosis. First, the cyclic frequency components associated with the fault characteristics in the composite fault vibration signals were detected by using cyclic spectrum analysis to construct cyclic frequency sets of different target types. After that, an improved algorithm for adaptively selecting the optimal filter length of CYCBD with the third-order cumulative sparsity(TCS) as the index was proposed according to different types of cyclic frequency sets, so as to better obtain the optimal filter length containing different fault shock components. Finally, a new 1.5-dimensional derivative spectrum was proposed for feature enhancement to improve the signal-to-noise ratio and to analyze the prominent fault feature frequencies in the spectrum to discriminate the fault types. The algorithm was validated by simulated signals and fault experimental platform data, and the results show that the method may achieve the accurate separation and diagnosis of compound faults in gearboxes.